Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating a content stack utilizing one or more machine-learning models. In some implementations, the disclosed systems generate and provide, to a user account, a content stack that includes content items corresponding to a topic prompt for the user account. For instance, in some implementations, the disclosed systems utilize content-based signals and account-based signals to generate an account-specific stack formulation graph that represents a plurality of content items and relationships of the content items with each other and with the user account. Additionally, in some implementations, the disclosed systems analyze the account-specific stack formulation graph to generate a content stack from the plurality of content items, the content stack comprising a set of content items corresponding to the topic prompt.

Abstract: A computer-implemented method for upmixing audiovisual data can include obtaining audiovisual data including input audio data and video data accompanying the input audio data. Each frame of the video data can depict only a portion of a larger scene. The input audio data can have a first number of audio channels. The computer-implemented method can include providing the audiovisual data as input to a machine-learned audiovisual upmixing model. The audiovisual upmixing model can include a sequence-to-sequence model configured to model a respective location of one or more audio sources within the larger scene over multiple frames of the video data. The computer-implemented method can include receiving upmixed audio data from the audiovisual upmixing model.

Abstract: Network elements and methods of use are described herein, including a network element comprising one or more demodulator and one or more modulator. The one or more demodulator is configured to receive first and second input signals and extract first phase and amplitude signals from the first input signal and second phase and amplitude signals from the second input signal. The first input signal has first input data. The second input signal has second input data. The first and second input data are encoded in a first modulation format. The one or more modulator is configured to modulate the first phase signal, the first amplitude signal, the second phase signal, and the second amplitude signal onto an output signal such that the output signal is encoded in a second modulation format. The output signal has a carrier frequency in a range between 500 Gigahertz (GHz) and 10 Terahertz (THz).

Abstract: Transport networks, network elements, and methods of use are described herein, including a transmitter comprising a client-side input, transmitter circuitry, and antennas. The client-side input is configured to receive baseband signals having client data encoded therein. The transmitter circuitry is configured to receive the baseband signals from the client-side input and generate antenna feed signals based on the baseband signals. The antennas are configured to receive the antenna feed signals from the transmitter circuitry, generate radiated signals based on the antenna feed signals, and couple the radiated signals into a hollow waveguide. Each of the radiated signals is a radiated electromagnetic wave configured for coherent detection and has a frequency in a range between 300 Gigahertz (GHz) and 10 Terahertz (THz).

Abstract: Transport networks, network elements, and methods of use are described herein, including a transmitter comprising a client-side input, transmitter circuitry, and antennas. The client-side input is configured to receive baseband signals having client data encoded therein. The transmitter circuitry is configured to receive the baseband signals from the client-side input and generate antenna feed signals based on the baseband signals. The antennas are configured to receive the antenna feed signals from the transmitter circuitry, generate radiated signals based on the antenna feed signals, and couple the radiated signals into a hollow waveguide. Each of the radiated signals is a radiated electromagnetic wave configured for coherent detection and has a frequency in a range between 300 Gigahertz (GHz) and 10 Terahertz (THz).

Abstract: A computer-implemented method for upmixing audiovisual data can include obtaining audiovisual data including input audio data and video data accompanying the input audio data. Each frame of the video data can depict only a portion of a larger scene. The input audio data can have a first number of audio channels. The computer-implemented method can include providing the audiovisual data as input to a machine-learned audiovisual upmixing model. The audiovisual upmixing model can include a sequence-to-sequence model configured to model a respective location of one or more audio sources within the larger scene over multiple frames of the video data. The computer-implemented method can include receiving upmixed audio data from the audiovisual upmixing model. The upmixed audio data can have a second number of audio channels. The second number of audio channels can be greater than the first number of audio channels.

Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating and providing an aggregated feed interface that includes or presents aggregate summaries for thread data across multiple data feeds of a user account. In some embodiments, the disclosed systems generate an aggregate summary by extracting digital communications and other thread data from a variety of data feeds across multiple computer applications and by utilizing a summary generation model to generate an aggregate summary that combines and condenses the thread data from the different data feeds. For example, the disclosed systems determine topics associated with thread data from various data feeds and generates different aggregate summaries for different topics.

Abstract: Disclosed herein are methods and systems for a training a model for real-time patient diagnosis. A system may include a computer configured to receive audio data and video data of a clinical encounter, the audio data comprising spoken words by an entity and the video data depicting the entity; retrieve clinical data regarding the entity; execute a model using the words of the audio data and the retrieved clinical data regarding the entity as input, the execution causing the model to output a plurality of clinical diagnoses for the entity; concurrently render the corresponding video data and audio data and the plurality of clinical diagnoses via a computing device associated with a user; and store an indication of a selected clinical diagnosis of the plurality of clinical diagnoses responsive to receiving a selection of the clinical diagnosis at the computing device.

Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating and providing an intelligent assistant interface that integrates with a large language model and a knowledge graph to adaptively change its appearance for presenting and interacting with different content items from various sources. In some embodiments, the disclosed systems provide an intelligent assistant interface that includes a set of interface elements selectable to interact with a large language model. Based on an input intent, the disclosed systems can utilize the large language model to analyze a knowledge graph for accessing and/or generating content items for display within the intelligent assistant interface.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating a content stack utilizing one or more machine-learning models. In some implementations, the disclosed systems generate and provide, to a user account, a content stack that includes content items corresponding to a topic prompt for the user account. For instance, in some implementations, the disclosed systems utilize content-based signals and account-based signals to generate an account-specific stack formulation graph that represents a plurality of content items and relationships of the content items with each other and with the user account. Additionally, in some implementations, the disclosed systems analyze the account-specific stack formulation graph to generate a content stack from the plurality of content items, the content stack comprising a set of content items corresponding to the topic prompt.

Abstract: A high-efficient central chiller plant system with variable load by phase change material thermal energy storage includes a refrigeration unit and a phase change thermal energy storage. The refrigeration unit operates under the highest COP. If the refrigerating output is higher than the demand of cooling load, the phase change thermal energy storage stores energy by the phase change. Contrarily, if the refrigerating output cannot meet the demand of cooling load, the phase change thermal energy storage releases energy to supply the insufficient cooling load of the refrigeration unit. So that users can set the operation strategy of the refrigeration unit according to the usage statistics, and let the refrigeration unit operate efficiently with the cooperation of the phase change thermal energy storage, thereby effectively improving the energy efficiency of the system operation to save energy. Compared with the existing central chiller system, it saves energy more than 40%-70%.

Abstract: A computer-implemented method for upmixing audiovisual data can include obtaining audiovisual data including input audio data and video data accompanying the input audio data. Each frame of the video data can depict only a portion of a larger scene. The input audio data can have a first number of audio channels. The computer-implemented method can include providing the audiovisual data as input to a machine-learned audiovisual upmixing model. The audiovisual upmixing model can include a sequence-to-sequence model configured to model a respective location of one or more audio sources within the larger scene over multiple frames of the video data. The computer-implemented method can include receiving upmixed audio data from the audiovisual upmixing model. The upmixed audio data can have a second number of audio channels. The second number of audio channels can be greater than the first number of audio channels.

Abstract: High-efficient central chiller plant system with variable load by phase change material thermal energy storage, comprising a refrigeration unit and a phase change thermal energy storage. The refrigeration unit operates under the highest COP. If the refrigerating output is higher than the demand of cooling load, the phase change thermal energy storage stores energy by the phase change. Contrarily, if the refrigerating output cannot meet the demand of cooling load, the phase change thermal energy storage releases energy to supply the insufficient cooling load of the refrigeration unit. So that users can set the operation strategy of the refrigeration unit according to the usage statistics, and let the refrigeration unit operate efficiently with the cooperation of the phase change thermal energy storage, thereby effectively improving the energy efficiency of the system operation to save energy. Compared with the existing central chiller system, it saves energy more than 40%-70%.

Abstract: Methods are provided for generating training triplets that can be used to train multidimensional embeddings to represent the semantic content of non-speech sounds present in a corpus of audio recordings. These training triplets can be used with a triplet loss function to train the multidimensional embeddings such that the embeddings can be used to cluster the contents of a corpus of audio recordings, to facilitate a query-by-example lookup from the corpus, to allow a small number of manually-labeled audio recordings to be generalized, or to facilitate some other audio classification task. The triplet sampling methods may be used individually or collectively, and each represent a respective heuristic about the semantic structure of audio recordings.

Abstract: Power management may be performed in a basic input/output system (BIOS) of an information handling system to provide flexibility in system operation. The BIOS may include a user setting specifying a power level, and that power level used to control a central processing unit (CPU) or other hardware components. For example, the BIOS power level may specify a sustained power setting for the CPU. One example sustained power setting is the PL1 value, which is a fixed value that specifies a thermal design power (TDP) for the CPU. As another example, the power level may specify whether to enable and/or disable a discrete graphics processing unit (GPU) or other hardware component operating from the same battery as the CPU.

Abstract: Methods are provided for generating training triplets that can be used to train multidimensional embeddings to represent the semantic content of non-speech sounds present in a corpus of audio recordings. These training triplets can be used with a triplet loss function to train the multidimensional embeddings such that the embeddings can be used to cluster the contents of a corpus of audio recordings, to facilitate a query-by-example lookup from the corpus, to allow a small number of manually-labeled audio recordings to be generalized, or to facilitate some other audio classification task. The triplet sampling methods may be used individually or collectively, and each represent a respective heuristic about the semantic structure of audio recordings.

Abstract: Power management may be performed in a basic input/output system (BIOS) of an information handling system to provide flexibility in system operation. The BIOS may include a user setting specifying a power level, and that power level used to control a central processing unit (CPU) or other hardware components. For example, the BIOS power level may specify a sustained power setting for the CPU. One example sustained power setting is the PL1 value, which is a fixed value that specifies a thermal design power (TDP) for the CPU. As another example, the power level may specify whether to enable and/or disable a discrete graphics processing unit (GPU) or other hardware component operating from the same battery as the CPU.

Abstract: Improved techniques and systems are disclosed for determining the components of resistance experienced by a wearer of a wearable device engaged in an activity such as bicycling or running. By monitoring data using the wearable device, improved estimates can be derived for various factors contributing to the resistance experienced by the user in the course of the activity. Using these improved estimates, data sampling rates may be reduced for some or all of the monitored data.

Abstract: Improved techniques and systems are disclosed for determining the components of resistance experienced by a wearer of a wearable device engaged in an activity such as bicycling or running. By monitoring data using the wearable device, improved estimates can be derived for various factors contributing to the resistance experienced by the user in the course of the activity. Using these improved estimates, data sampling rates may be reduced for some or all of the monitored data.

Abstract: An electrophoresis running tank assembly uses two opposed rows of LEDs to illuminate DNA-containing gel on a transparent tray positioned between the rows. A respective cylindrical lens is positioned horizontally between each row and a respective edge of the tray. The optical axis of the illumination light is midway between a bottom surface of the gel tray and a top surface of the gel.